Pain
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Using cross-sectional data from the United States, England, China, and India, we examined the relationship between education and frequent pain, alongside the modification role of gender in this relationship. We further examined patterns of 3 pain dimensions among participants who reported frequent pain, including pain severity, interference with daily activities, and medication use (these pain dimension questions were not administered in all countries). Our analytical sample included 92,204 participants aged 50 years and above. ⋯ In the United States, these associations were stronger among women. Our findings highlight the prevalent pain among middle-aged and older adults in these 4 countries and emphasize the potentially protective role of higher education on frequent pain, with nuanced gender differences across different settings. This underscores the need for tailored strategies considering educational and gender differences to improve pain management and awareness.
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Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.
Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. ⋯ Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.
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Chronic pain is common among children and adolescents; however, the diagnoses in the newly developed 11th revision of the International Classification of Diseases (ICD-11) chronic pain chapter are based on adult criteria, overlooking pediatric neurodevelopmental differences. The chronic pain diagnoses have demonstrated good clinical applicability in adults, but to date, no field study has examined these diagnoses to the most specific diagnostic level in a pediatric sample. The current study aimed to explore pediatric representation within the ICD-11, with focus on chronic primary pain. ⋯ The latter also exhibited the lowest agreement between HCPs and algorithm. The current study underscores the need for evidence-based improvements to the ICD-11 diagnostic criteria in pediatrics. Developing pediatric coding notes could improve the visibility of patients internationally and improve the likelihood of receiving reimbursement for necessary treatments through accurate coding.